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An unsupervised learning method is proposed for variable selection and its performance assessed using three typical QSAR data sets. The aims of this procedure are to generate a subset of descriptors from any given data set in which the resultant variables are relevant, redundancy is eliminated, and multicollinearity is reduced. Continuum regression, an(More)
The linear interaction energy (LIE) method has been applied to the calculation of the binding free energies of 15 inhibitors of the enzyme neuraminidase. This is a particularly challenging system for this methodology since the protein conformation and the number of tightly bound water molecules in the active site are known to change for different(More)
We describe the use of Bayesian regularized artificial neural networks (BRANNs) coupled with automatic relevance determination (ARD) in the development of quantitative structure-activity relationship (QSAR) models. These BRANN-ARD networks have the potential to solve a number of problems which arise in QSAR modeling such as the following: choice of model;(More)
Octopamine (OA) hyperpolarizes leech Leydig neurones but response amplitudes and thresholds vary. Associations between response, OA dose and other variables [initial resting potential, action potential (AP) frequency and amplitude, temperature, microelectrode resistance, position of ganglion along nerve cord, animal, treatment order] were examined using(More)
Destruxin A, a cyclohexadepsipeptide related to the enniatins and beauvericin, exhibits ionophoric properties. Calcium ion mobilization across liposomal membrane barriers, for example, has been demonstrated using the calcium ion-sensitive dyes Arsenazo III and Fura-2. Initial molecular mechanics/molecular dynamics calculations indicate the potential for(More)
It has been shown that water solubility and octanol/water partition coefficient for a large diverse set of compounds can be predicted simultaneously using molecular descriptors derived solely from a two dimensional representation of molecular structure. These properties have been modelled using multiple linear regression, artificial neural networks and a(More)
BCUT [Burden, CAS, and University of Texas] descriptors, defined as eigenvalues of modified connectivity matrices, have traditionally been applied to drug design tasks such as defining receptor relevant subspaces to assist in compound selections. In this paper we present studies of consensus neural networks trained on BCUTs to discriminate compounds with(More)
In the past, neural networks have been viewed as classiÿcation and regression systems whose internal representations were extremely diicult to interpret. It is now becoming apparent that algorithms can be designed which extract understandable representations from trained neural networks , enabling them to be used for data mining, i.e. the discovery and(More)
A series of neural networks has been trained, using consensus methods, to recognize compounds that act at biological targets belonging to specific gene families. The MDDR database was used to provide compounds targeted against gene families and sets of randomly selected molecules. BCUT parameters were employed as input descriptors that encode structural(More)
Linear discriminant analysis and a committee of neural networks have been applied to recognise compounds that act at biological targets belonging to a specific gene family, protein kinases. The MDDR database was used to provide compounds targeted against this family and sets of randomly selected molecules. BCUT parameters were employed as input descriptors(More)